Abstract

Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to “teach” robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinematics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.

Figures

Illustrations of the proposed metrics of anthropomorphism. Human arm is the right kinematic chain that consists of two links, while the hypothetical robot arm is the left kinematic chain that has 5 links.

(a) A trajectory tracking example that involves a 20DOF hyper-redundant robot arm, the end-effector of which should attain same position and orientation with the human arm end-effector. (b) A trajectory tracking example that involves a hyper-redundant robot arm-hand system that consists of a 23DOF hyper-redundant arm and a five-fingered hand with size equal to the 110% of the human hand size. For this example, the robot fingertips should track the human fingertip positions. The lines denote the human trajectories, and the markers the derived robot trajectories.

Mapping human to robot motion experiments. The proposed methodology has been used to extract humanlike robot poses for three different applications. (a) The real-time teleoperation of a simulated Mitsubishi PA10–DLR/HIT II arm-hand system. (b) The teleoperation of a simulated arm-hand system that consists of a hyper-redundant robot arm (21DOF) and the DLR/HIT II robot hand. (c) An example of autonomous, anthropomorphic grasp planning using the Mitsubishi PA10–DLR/HIT II arm-hand system.

Comparison of different mapping methodologies for the case of the Barrett WAM robot arm [44]. The left kinematic chain for all cases is the Barrett WAM robot arm which is longer that then human arm (right kinematic chain). The two kinematic chains are depicted with an offset in the x-axis of their base frames, in order to facilitate comparisons. The sphere denotes the desired end-effector position for the robot arm. For all cases, the human arm pose is the same.

Deriving humanlike poses for m-fingered robot hands with size equal to the 110% of the human hand size. The selection of the desired robot fingertip positions (crosses) is performed via interpolation between the human fingertips positions (circles).

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